基于混沌与量子粒子群算法相结合的负荷模型参数辨识研究

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摘要负荷建模是电力系统建模中亟待解决的难题。负荷特性数据、负荷模型结构以及参数辨识是影响实测负荷建模结果的重要因素。本文提出了混沌与量子粒子群算法相结合的负荷模型参数辨识方法。实测数据验证结果表明，该方法相对于常用的粒子群算法及量子粒子群算法在计算精度、收敛速度等方面都具有明显优势，应用于负荷模型参数辨识提高了负荷模型的准确性。

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	王振树
	卞绍润
	刘晓宇
	于凯
	石云鹏

关键词 ：混沌优化算法, 量子粒子群算法, 故障录波, 参数辨识, 负荷建模

Abstract：Load modeling has become a critical problem that is urgent to be solved in power system modeling. Precise field measurement data,proper load model structure and accurate parameter identification are three key factors that can affect the results of the measurement-based load modeling. In this paper,CQDPSO algorithm,a hybrid optimization algorithm that combines chaotic optimization algorithm(COA) and quantum delta-potential-well-based particle swarm optimization(QDPSO) algorithm is proposed to identify the parameters of the selected load model. Finally,simulation results using field measurement data illustrate that the proposed method has advantages in calculation precision,convergence speed when compared with particle swarm optimization(PSO) algorithm and QDPSO algorithm. The application of CQDPSO algorithm in load model parameter identification can improve the accuracy of load models.

Key words： Chaotic optimization algorithm(COA) quantum delta-potential-well-based particle swarm optimization(QDPSO) algorithm power fault recorder parameter identification load modeling

收稿日期: 2014-05-28 出版日期: 2015-01-22

PACS:

TM714

基金资助:国家自然科学基金（51377099）和山东省自然科学基金（ZR2011- EEM017）资助项目。

作者简介: 王振树男，1963年生，教授，研究方向为电力系统运行控制。卞绍润男，1989年生，硕士，研究方向为负荷建模。

引用本文:

王振树，卞绍润，刘晓宇，于凯，石云鹏. 基于混沌与量子粒子群算法相结合的负荷模型参数辨识研究[J]. 电工技术学报, 2014, 29(12): 211-217. Wang Zhenshu,Bian Shaorun,Liu Xiaoyu,Yu Kai,Shi Yunpeng. Research on Load Model Parameter Identification Based on the CQDPSO Algorithm. Transactions of China Electrotechnical Society, 2014, 29(12): 211-217.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2014/V29/I12/211

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